World Happiness Report

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, and the third on April 23, 2015. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness. They reflect a new worldwide demand for more attention to happiness as a criteria for government policy.

SUMMARY

Background

The world has come a long way since the first World Happiness Report launched in 2012. Increasingly happiness is considered a proper measure of social progress and goal of public policy. A rapidly increasing number of national and local governments are using happiness data and research in their search for policies that could enable people to live better lives. Governments are measuring subjective well-being, and using well-being research as a guide to the design of public spaces and the delivery of public services.

Harnessing Happiness Data and Research to Improve Sustainable Development

The year 2015 is a watershed for humanity, with the pending adoption by UN member states of Sustainable Development Goals (SDGs) in September to help guide the world community towards a more inclusive and sustainable pattern of global development. The concepts of happiness and well-being are very likely to help guide progress towards sustainable development.

Sustainable development is a normative concept, calling for all societies to balance economic, social, and environmental objectives. When countries pursue GDP in a lopsided manner, overriding social and environmental objectives, the results often negatively impact human well- being. The SDGs are designed to help countries to achieve economic, social, and environmental objectives in harmony, thereby leading to higher levels of well-being for the present and future generations.

The SDGs will include goals, targets and quantitative indicators. The Sustainable Development Solutions Network, in its recommendations on the selection of SDG indicators, has strongly recommended the inclusion of indicators of Subjective Well-being and Positive Mood Affect to help guide and measure the progress towards the SDGs. We find considerable support of many governments and experts regarding the inclusion of such happiness indicators for the SDGs. The World Happiness Report 2015 once again underscores the fruitfulness of using happiness measurements for guiding policy making and for helping to assess the overall well-being in each society.

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FREQUENTLY ASKED QUESTIONS

What is the original source of the data for Figure 2.2? How are the rankings calculated?

The rankings in figure 2.2 use data that come from the Gallup World Poll (for more information see the Gallup World Poll methodology). The rankings are based on answers to the main life evaluation question asked in the poll. This is called the Cantril ladder: it asks respondents to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale.

The rankings are from nationally representative samples, for the years 2012-2014. They are based entirely on the survey scores, using the Gallup weights to make the estimates representative. The sub-bars show the estimated extent to which each of six factors – levels of GDP, life expectancy, generosity, social support, freedom, and corruption – contribute to making life evaluations higher in each country than they are in Dystopia. They have no impact on the total score reported for each country, but instead are just a way of explaining for each country the implications of the model estimated in Table 2.1. People often ask why some countries rank higher than others – the sub-bars (including the residuals, which show what is not explained) are an attempt to provide an answer to that question.

What is your sample size for figure 2.2?

We use the most recent years in order to provide an up-to-date measure, and to measure changes over time. We combine data from the years 2012-2014 to make the sample size large enough to reduce the random sampling errors. (The horizontal lines at the right-hand end of each of the main bars show the 95% confidence interval for the estimate.) The typical annual sample is 1,000 people. So if a country had surveys in each year, then the sample size would be 3,000 people. However, there are many countries that have not had annual surveys, and some of the 2014 surveys were not available when we began analysis on December 31, 2014. We strive to keep the sample size for each country at 2,000 people or more. If there are not at least two available surveys from the 2012-2014 period, then we use 2011 survey results to bring the sample size up to 2,000, but in no case do we go further back than 2011.

Is this sample size really big enough to calculate rankings?

A sample size of 2,000 to 3,000 is large enough to give a fairly good estimate at the national level. It is not large enough to give precision for sub-populations, which is why in Chapter 3 we use data from all available surveys, from 2005 through 2014, to provide samples of sufficient size for our splits by age and gender.

What is the confidence interval?

The confidence intervals, as shown by the horizontal lines at the right-hand end of the country bars, show the range of values within which there is a 95% likelihood of the population mean being located.

Where do the sub-bars come from for each of the six explanatory factors?

The sub-bars show, tentatively, what share of a country’s overall score can be explained by each of the six factors in Table 2.1. The sub-bars are calculated by multiplying average national data for the period 2012-2014 for each of the six factors (minus the value of that variable in Dystopia) by the coefficient on this variable in the first equation of Table 2.1. This product then shows the average amount by which the overall happiness score (the life evaluation) is higher in a country because they perform better than Dystopia on that variable.

To describe an example, let’s look at the variable of life expectancy in the case of Brazil. First we calculate the number of years by which healthy life expectancy in Brazil exceeds that in the country with the lowest life expectancy. Then we multiply this number of years by the estimated Table 2.1 coefficient for life expectancy. This product then shows the average amount by which the overall happiness score (the life evaluation) is higher in Brazil because life expectancy is higher there than it is in the country with the lowest life expectancy. This process is repeated for each country and each of the six variables.

Because of the way they were constructed, these six bars will in total always be less than each country’s average life evaluation. They also will not alter in any way the width of the overall life evaluation bar on which the rankings are based. The difference between what is attributed to the six factors and the total life evaluations is the sum of two parts. These are the average life evaluations in Dystopia, and each country’s residual. You may find the following FAQs useful: What is Dystopia? What are the residuals?

What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.

What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2012-2014 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2012- 2014 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.3, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 2.10 on the 0 to 10 scale.

Why do we use these six factors to explain life evaluations?

The variables used reflect what has been broadly found in the research literature to be important in explaining national-level differences in life evaluations. Some important variables, such as unemployment or inequality, do not appear because comparable international data are not yet available for the full sample of countries. The variables are intended to illustrate important lines of correlation rather than to reflect clean causal estimates, since some of the data are drawn from the same survey sources, some are correlated with each other (or with other important factors for which we do not have measures), and in several instances there are likely to be two-way relations between life evaluations and the chosen variables (for example, healthy people are overall happier, but as Chapter 4 in the World Happiness Report 2013 demonstrated, happier people are overall healthier).

What is a data “wave”?

Gallup refers to the surveys in each calendar year as being part of that year’s survey wave. Not every country is surveyed every year, and thus the size of the survey waves also varies from year to year.

Can I download any of the data used in the Report?

The online data appendices show how the data are constructed, and include the main national and regional averages underlying the figures and tables in Chapter 2 and 3. Those wishing access to more detailed data from the Gallup World Poll should contact Gallup directly: